Characteristics of the Unexpected Message Queue of MPI Applications

Author(s):  
Rainer Keller ◽  
Richard L. Graham
Author(s):  
Istabraq M. Al-Joboury ◽  
Emad H. Al-Hemiary

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.


Author(s):  
Jesus Carretero ◽  
Javier Garcia-Blas ◽  
David E. Singh ◽  
Florin Isaila ◽  
Thomas Fahringer ◽  
...  
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2015 ◽  
Vol 25 (03) ◽  
pp. 1541002 ◽  
Author(s):  
Henri Casanova ◽  
Anshul Gupta ◽  
Frédéric Suter

The off-line (or post-mortem) analysis of execution event traces is a popular approach to understand the performance of HPC applications that use the message passing paradigm. Combining this analysis with simulation makes it possible to “replay” the application execution to explore “what if?” scenarios, e.g., assessing application performance in a range of (hypothetical) execution environments. However, such off-line analysis faces scalability issues for acquiring, storing, or replaying large event traces. We first present two previously proposed and complementary frameworks for off-line replaying of MPI application event traces, each with its own objectives and limitations. We then describe how these frameworks can be combined so as to capitalize on their respective strengths while alleviating several of their limitations. We claim that the combined framework affords levels of scalability that are beyond that achievable by either one of the two individual frameworks. We evaluate this framework to illustrate the benefits of the proposed combination for a more scalable off-line analysis of MPI applications.


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